Bone Fracture Detection Using Deep Supervised Learning from Radiological Images: A Paradigm Shift

Meena, Tanushree; Roy, Sudipta

doi:10.3390/diagnostics12102420

Cited by 76 publications

(29 citation statements)

References 52 publications

(50 reference statements)

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“…Another challenging issue for our novel technology in the healthcare industry is transparency. It is difficult to interpret some medical imaging and analysis with the most advanced algorithms, such as deep learning [ 131 ]. This makes us unable to answer the question ‘why’, which is very important in the healthcare and biomedical industry.…”

Section: Ethical Implicationsmentioning

confidence: 99%

Demystifying Supervised Learning in Healthcare 4.0: A New Reality of Transforming Diagnostic Medicine

Roy¹,

Meena²,

Lim

2022

Diagnostics

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The global healthcare sector continues to grow rapidly and is reflected as one of the fastest-growing sectors in the fourth industrial revolution (4.0). The majority of the healthcare industry still uses labor-intensive, time-consuming, and error-prone traditional, manual, and manpower-based methods. This review addresses the current paradigm, the potential for new scientific discoveries, the technological state of preparation, the potential for supervised machine learning (SML) prospects in various healthcare sectors, and ethical issues. The effectiveness and potential for innovation of disease diagnosis, personalized medicine, clinical trials, non-invasive image analysis, drug discovery, patient care services, remote patient monitoring, hospital data, and nanotechnology in various learning-based automation in healthcare along with the requirement for explainable artificial intelligence (AI) in healthcare are evaluated. In order to understand the potential architecture of non-invasive treatment, a thorough study of medical imaging analysis from a technical point of view is presented. This study also represents new thinking and developments that will push the boundaries and increase the opportunity for healthcare through AI and SML in the near future. Nowadays, SML-based applications require a lot of data quality awareness as healthcare is data-heavy, and knowledge management is paramount. Nowadays, SML in biomedical and healthcare developments needs skills, quality data consciousness for data-intensive study, and a knowledge-centric health management system. As a result, the merits, demerits, and precautions need to take ethics and the other effects of AI and SML into consideration. The overall insight in this paper will help researchers in academia and industry to understand and address the future research that needs to be discussed on SML in the healthcare and biomedical sectors.

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Section: Ethical Implicationsmentioning

confidence: 99%

Demystifying Supervised Learning in Healthcare 4.0: A New Reality of Transforming Diagnostic Medicine

Roy¹,

Meena²,

Lim

2022

Diagnostics

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“…How other datasets will be precisely segmented will not be addressed here. Meena and Roy [20] discuss the detection of bone fractures using deep supervised learning from X-ray imagery. Failed fractures are common prognostic failures in case of accident or emergency.…”

Section: Literature Surveymentioning

confidence: 99%

ColoRectalCADx: Expeditious Recognition of Colorectal Cancer with Integrated Convolutional Neural Networks and Visual Explanations Using Mixed Dataset Evidence

Raju

Jayavel

Rajalakshmi

2022

Computational and Mathematical Methods in Medicine

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Colorectal cancer typically affects the gastrointestinal tract within the human body. Colonoscopy is one of the most accurate methods of detecting cancer. The current system facilitates the identification of cancer by computer-assisted diagnosis (CADx) systems with a limited number of deep learning methods. It does not imply the depiction of mixed datasets for the functioning of the system. The proposed system, called ColoRectalCADx, is supported by deep learning (DL) models suitable for cancer research. The CADx system comprises five stages: convolutional neural networks (CNN), support vector machine (SVM), long short-term memory (LSTM), visual explanation such as gradient-weighted class activation mapping (Grad-CAM), and semantic segmentation phases. Here, the key components of the CADx system are equipped with 9 individual and 12 integrated CNNs, implying that the system consists mainly of investigational experiments with a total of 21 CNNs. In the subsequent phase, the CADx has a combination of CNNs of concatenated transfer learning functions associated with the machine SVM classification. Additional classification is applied to ensure effective transfer of results from CNN to LSTM. The system is mainly made up of a combination of CVC Clinic DB, Kvasir2, and Hyper Kvasir input as a mixed dataset. After CNN and LSTM, in advanced stage, malignancies are detected by using a better polyp recognition technique with Grad-CAM and semantic segmentation using U-Net. CADx results have been stored on Google Cloud for record retention. In these experiments, among all the CNNs, the individual CNN DenseNet-201 (87.1% training and 84.7% testing accuracies) and the integrated CNN ADaDR-22 (84.61% training and 82.17% testing accuracies) were the most efficient for cancer detection with the CNN+LSTM model. ColoRectalCADx accurately identifies cancer through individual CNN DesnseNet-201 and integrated CNN ADaDR-22. In Grad-CAM’s visual explanations, CNN DenseNet-201 displays precise visualization of polyps, and CNN U-Net provides precise malignant polyps.

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“…It upholds arm and shoulder movement as it is the longest bone in the arm. Figure (1) shows the humerus bone. According to World Health Organization research, humeral shaft fractures account for about 3% of all orthopedic injuries and cause major monetary and misfortune for society.…”

Section: Introductionmentioning

confidence: 99%

“…Locally, they mostly affect young, socio economically active age groups, with the majority of cases being brought on by motor accidents. It demonstrates to us how prevalent bone fractures are as a medical problem, with a nonnegligible cost of care that can rise exponentially as life moves more quickly than we can fathom.The healing process is complicated after a bone fracture, which is a common injury 1 . One of the few tissues that can recover without leaving a fibrous scar is bone.…”

Section: Introductionmentioning

confidence: 99%

HD-NET: Humerus deep-net for humerus fracture and bony callus formation analysis

Abdullah

Shoaib

Arshad

et al. 2023

Preprint

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When employing x-ray images, fracture identification in orthopaedics is a difficult task. A large percentage of humerus fracture patients are seen in hospitals, particularly in their emergency departments. Similar to this, after a fracture, accurate callus production monitoring is crucial for bone healing. Thus, a fractured patient’s diagnosis and therapy must be accurate and administered promptly. This work investigates the use of deep learning on X-ray images of the humerus for fracture snd bone callus formation analysis to help physicians in the diagnosis of such fractures, especially in emergency settings.This study is named HD-NET, which stands for Humerus Deep Net. The framework includes image enhancement using a Gaussian filter and histogram equalization, two-stage object detection, image super-resolution, U-NET segmentation with feature recalibration. Finally, an LSTM with a sequence length of 2 is used to analyze callus formation at the fracture site. The LSTM takes the segmented area as input and outputs a prediction for the stage of healing and potential complications. The proposed framework was evaluated on a combination of the MURA dataset and a self-collected dataset.Results demonstrated that in terms of specificity, sensitivity, and accuracy, the suggested framework performed better than earlier studies. This research can be expanded to different bone types and is useful for orthopaedic practitioners.

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Bone Fracture Detection Using Deep Supervised Learning from Radiological Images: A Paradigm Shift

Cited by 76 publications

References 52 publications

Demystifying Supervised Learning in Healthcare 4.0: A New Reality of Transforming Diagnostic Medicine

Demystifying Supervised Learning in Healthcare 4.0: A New Reality of Transforming Diagnostic Medicine

ColoRectalCADx: Expeditious Recognition of Colorectal Cancer with Integrated Convolutional Neural Networks and Visual Explanations Using Mixed Dataset Evidence

HD-NET: Humerus deep-net for humerus fracture and bony callus formation analysis

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